Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

Table 7 Rank importance of risk factors in model 3, from the most important to the least

Variable	MARS	XGboost	SGB	LightGBM	Catboost	AVG
Uric acid	1	1	1	1	1	1
Age	1	2	2	2	4	2.2
Hemoglobin	4	4	6	3	3	4
Body mass index	7	3	3	7	2	4.4
Sport hours	3	8	4	5	13	6.6
Systolic blood pressure	5	6	20	4	5	8
Diastolic blood pressure	20	5	5	6	14	10
Alkaline phosphatase	20	7	11	12	10	12
γ-glutamyl transferase	20	13	8	9	12	12.4
Hs-C reactive protein	9	12	10	15	17	12.6
Fasting plasma glucose	20	9	20	10	7	13.2
Serum glutamic oxaloacetic transaminase	20	18	7	13	8	13.2
HDL-cholesterol	20	10	13	8	15	13.2
Drinking	20	14	12	16	6	13.6
White blood cell count	8	11	20	11	18	13.6
Sleep time	6	17	20	18	11	14.4
Serum glutamic pyruvic transaminase	20	16	14	14	9	14.6
Triglyceride	20	15	9	17	16	15.4
Gender	20	20	20	20	19	19.8
Smoking	20	20	20	20	20	20

AVG: Average of the rank of importance; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; HDL: High-density lipoprotein; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 3: Model 2 + inflammation factors.